Pierre Kleiber

Biomass, Size, and Trophic Status of Top Predators in the Pacific Ocean

Fisheries have removed at least 50 million tons of tuna and other top-level predators from the Pacific Ocean pelagic ecosystem since 1950, leading to concerns about a catastrophic reduction in population biomass and the collapse of oceanic food chains. We analyzed all available data from Pacific tuna fisheries for 1950–2004 to provide comprehensive estimates of fishery impacts on population biomass and size structure. Current biomass ranges among species from 36 to 91% of the biomass predicted in the absence of fishing, a level consistent with or higher than standard fisheries management targets. Fish larger than 175 centimeters fork length have decreased from 5% to approximately 1% of the total population. The trophic level of the catch has decreased slightly, but there is no detectable decrease in the trophic level of the population. These results indicate substantial, though not catastrophic, impacts of fisheries on these top-level predators and minor impacts on the ecosystem in the Pacific Ocean.

Fisheries: Decline of Pacific tuna populations exaggerated?

industrial fisheries in the Pacific Ocean and elsewhere since the 1950s. In their analysis of Japanese longline-fishery catchper-unit-effort (CPUE) data, Myers and Worm conclude that the community (species-aggregated) biomass of large pelagic fish, mainly tunas, was reduced by 80% during the first 15 years of exploitation and is now at 10% of pre-industrial levels. We show here that an assumption critical to this conclusion — namely, that Japanese longline CPUE acts as an accurate index of community biomass — is invalid. Our results indicate that biomass decline and fishing impacts are much less severe than is claimed by Myers and Worm. Interpretation of the species-aggregated CPUE as an index of community biomass rests on the assumption that catchability (a coefficient specifying the proportionality between CPUE and abundance) is constant across species and over time. The former is unrealistic because, among other things, the species have different depth distributions and hence different vulnerability to longline gear. The evolution of tuna longline fisheries in all oceans has seen changes in fishing strategies (and hence catchability) as different species have been targeted. In the early 1960s, Japanese longliners changed from targeting albacore (Thunnus alalunga) and yellowfin (T. albacares) for the canned-tuna market to bigeye (T. obesus) and yellowfin tuna for the Japanese sashimi market. Japanese longline CPUE for albacore declined rapidly not because of declining albacore abundance, but because of this change in species targeting. By contrast, Taiwanese longliners have consistently targeted albacore in subequatorial waters of all oceans, and their CPUE provides a better index of albacore abundance. These results show that CPUE has declined by 50% over 40 years in the South Pacific, but they do not replicate the rapid and much larger decline in CPUE in the 1960s evident in the Japanese data (Fig.1a). The Myers and Worm analysis excludes data from the equatorial Pacific, where the highest catches are taken and which is the core habitat for tropical tunas. When these data are included, yellowfin-tuna CPUE in the western Pacific is seen to decline by 70% over 50 years, during which time annual catches by longline and other methods increase from insignificant levels in the early 1950s to more than 400,000 tonnes by the late 1990s (Fig. 1b). By contrast, the CPUE for bigeye tuna has been stable for over 40 years, despite continuously increasing catch (Fig. 1c). Changes in fishing strategies designed to target the deeper-swimming and higher-value bigeye tuna occurred during the 1970s (ref. 3), making it unlikely that CPUE accurately reflects changes in abundance for either species unless it is adjusted to account for the shift in targeting. Unadjusted Japanese longline CPUE tends to overestimate abundance decline for yellowfin tuna and underestimate abundance decline for bigeye tuna. Stock assessments rely on a range of data in addition to CPUE, including catch, size composition, tagging and biological data. When stock-assessment models 6 that consider all the available data are applied to Pacific tunas, fishery-induced declines in abundance during the 1950s and 1960s of the magnitude proposed by Myers and Worm are found to be extremely unlikely. Moreover, where declines do occur, they are not, as claimed by Myers and Worm, due exclusively to fishing. It is impossible, for example, under conventional populationdynamics theory to attribute the pre-1970 decline in yellowfin CPUE to fishing at a time when the total catches were less than one-tenth of today’s catches. In summary, the trends in catches and CPUE (Fig. 1) and the results of stock-assessment modelling show that the basic assumption of Myers and Worm that CPUE is proportional to brief communications arising

Generalized additive model and regression tree analyses of blue shark (Prionace glauca) catch rates by the Hawaii-based commercial longline fishery

Abstract Generalized additive model (GAM) and regression tree analyses were conducted with blue shark, Prionace glauca , catch rates (catch per set) as reported by National Marine Fisheries Service observers serving aboard Hawaii-based commercial longline vessels from March 1994 through December 1997 ( N =2010 longline sets). The objective was to use GAM and regression tree methodology to relate catch rates to a tractable suite of readily measured or computed variables. Because the predictor variables are also either provided in or easily computed from the logbooks that commercial vessels submit upon landing fish for sale, it is likely that a model or models fitted to accurate observer data could then be applied on a fleet-wide basis to serve as a standard of comparison for the logbooks. The GAM included nine spatio-temporal, environmental, and operational variables and explained 72.1% of the deviance of blue shark catch rates. Latitude exerted the strongest effects of any individual variable; longitude was the most influential variable when adjusted for the effects of all other factors. Relatively cold sea surface temperatures were associated with high catch rates. The initial regression tree included 68 terminal nodes and 11 predictors. It was refined to a final tree with 42 terminal nodes, which reduced the root mean deviance by 65.3%. The tree was partitioned first on latitude 26.6°N, and then branched out to reach terminal nodes after 2–8 additional partitionings. Sets south of this latitude were characterized by lower catch rates and partitionings on a greater number and variety of predictors. Northerly sets were characterized by higher and more variable blue shark catch rates. Predictions from the two analyses were highly correlated ( r =0.903, P ⪡0.001). Moreover, use of these methods in combination aided greatly in the interpretation of results. We conclude that GAM and regression tree analyses can be usefully employed in the assessment of blue shark catch rates in this fishery. We suggest that either or both of these models could serve as comparison standards for commercial logbooks.

Comparison of logbook reports of incidental blue shark catch rates by Hawaii-based longline vessels to fishery observer data by application of a generalized additive model

Abstract A generalized additive model (GAM) of blue shark, Prionace glauca, catch rates (catch per set) was fitted to data gathered by National Marine Fisheries Service (NMFS) observers stationed aboard Hawaii-based commercial longline vessels (N=2010 longline sets) from March 1994 to December 1997. Its coefficients were then applied to the values of predictor variables, which were also contained in logbook records that described the remainder of fishery-wide effort during the study period ( N=41 319 longline sets). The objective was to determine whether predictions generated by such a GAM could serve in lieu of observers on the large fraction of longline trips that do not carry an observer (approximately 95%). After deleting data considered false or inaccurate, much of which was associated with a small number of vessels, the relationship between catch rates as reported in logbooks and GAM predictions was expressed by log e (Y+1)=0.7952 log e (X+1)−0.0586 where Y is the catch rate (i.e., the number of blue shark caught per set) and X the GAM predictions (R 2 =0.307, N=40 243) . Patterns of correspondence between logbook trends and GAM predictions were further refined by plotting the trends according to the type of fishing effort (e.g., tuna- or swordfish-directed). The highest mean catch rates reported in logbooks, the highest mean GAM predictions, and the greatest differences between the two occurred consistently in mid-year on swordfish trips. In contrast, mean values from logbooks and mean GAM predictions were closest for tuna-directed effort, but this reflected an order of magnitude reduction in the scale of catch rates rather than closely similar trends. A bootstrapping algorithm developed for the GAM yielded an estimate of 23.9% under-reporting for the study period, with approximate 95% prediction limits of 15.4–28.9%. We conclude that prediction with a GAM fitted to fishery observer data is a useful monitoring technique for the Hawaii-based commercial longline fishery. It allowed us: to gain insight into fleet-wide and individual logbook reporting practices, to estimate the relationship between logbook data and predicted values, to characterize bias in this relationship, and to identify patterns specific to each major sector of the fishery.